@inproceedings{hossain-alve-2026-failure,
    title = "Failure at {BEA} 2026 Shared Task 1: One Pipeline, Three L1s: A Unified Language-Agnostic System for Vocabulary Difficulty Prediction",
    author = "Hossain, Abid  and
      Alve, Kamruzzaman Khan",
    editor = "Kochmar, Ekaterina  and
      Alhafni, Bashar  and
      Bann{\`o}, Stefano  and
      Bexte, Marie  and
      Burstein, Jill  and
      Horbach, Andrea  and
      Laarmann-Quante, Ronja  and
      Tack, Anais  and
      Yaneva, Victoria  and
      Yuan, Zheng",
    booktitle = "Proceedings of the 21st Workshop on Innovative Use of {NLP} for Building Educational Applications ({BEA} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.bea-1.73/",
    pages = "1041--1046",
    ISBN = "979-8-89176-409-5",
    abstract = "We present a unified, language-agnostic system for the BEA 2026 Shared Task on vocabulary difficulty prediction. The system uses a single training pipeline across Spanish, German, and Mandarin Chinese without any language-specific adaptation. Input features include serialized text fields and four scalar length-based features, processed using an XLM-RoBERTa encoder with attention-mask-weighted mean pooling. Hyperparameters are tuned with Optuna under reduced cross-validation, followed by full 5-fold training and checkpoint-based ensembling.Our approach improves over the official closed-track baseline across all three L1 conditions, demonstrating that a shared architecture and training strategy can yield consistent gains without language-specific engineering. Error analysis shows higher prediction error at difficulty extremes, suggesting a regression-to-the-mean tendency."
}